Benchmarking and Comparing AD-Related AI Methods Across Sites on a Standardized Dataset

在标准化数据集上跨站点对 AD 相关 AI 方法进行基准测试和比较

基本信息

  • 批准号:
    10825403
  • 负责人:
  • 金额:
    $ 35.75万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-09-15 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

Project Summary In response to PAR-19-269 “Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data (U01 Clinical Trial Not Allowed)”, our project unites experts in AD genomics, machine learning and AI (including deep learning), large-scale data integration, and international data harmonization to work in a carefully-designed Consortium Structure in close partnership with the NIH, ADSP, and NIAGADS. We will develop a suite of complementary big data analytic approaches for ultra-scale analysis of Alzheimer’s Disease (AD) genomic and phenotypic data. The vast data volumes now generated by the Alzheimer’s Disease Sequencing Project (ADSP), National Alzheimer’s Coordinating Center (NACC), Alzheimer’s Disease Neuroimaging Initiative (ADNI), Accelerating Medications Partnership AD (AMP-AD), and UK Biobank (UKBB), far exceed the capacity of all current analytic methods, which have not kept pace with the scale and speed of data collection. This vast amount of genetic and phenotypic data mandates new and more powerful algorithms to: (1) store, manage, and manipulate whole-genome sequences and associated data on an ever-growing scale; (2) discover novel AD risk and protective loci by merging informatics and AD genomics databases; (3) relate whole-genome changes to the ATN(v) biomarkers that now define biological AD. Our Ultrascale Machine Learning Initiative, or “ULTRA” - will offer new AI and deep learning tools to discover features in massive scale genomics data - relating whole genome data to biomarker features by merging all relevant data sources. Our team of experienced PIs will coordinate efforts across the U.S. to create these large-scale data analytic tools. Our MPI team and 6 Core Leads have decades of experience working together and with the AD community in pioneering machine learning methods for AD genetics and neuroimaging, including leadership of international neuroimaging consortia across the world. Dedicated Cores focus on Genomic, Imaging, and Cognitive Data Harmonization. Curated data will then be efficiently imported into AI approaches and informatics pipelines that will allow the AD research community to leverage ultra-scale, multidimensional genomic and phenotypic data from the ADSP, NACC, ADNI, AMP-AD, and others. Our work is organized by a carefully-designed and coordinated Consortium guided by all stake-holders, clinical leaders, and pioneering analysts in AD genomics and neuroimaging. Our ultrascale AI tools will advance AD genomics research and will include efforts in training, and a dedicated Drug Repurposing Core. This team effort will accelerate understanding of the genetic, molecular and neurobiological mechanisms of AD, yielding significant translational impact on disease and drug development.
项目总结

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer's disease.
  • DOI:
    10.1038/s41598-018-25377-w
  • 发表时间:
    2018-05-10
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Sohn D;Shpanskaya K;Lucas JE;Petrella JR;Saykin AJ;Tanzi RE;Samatova NF;Doraiswamy PM
  • 通讯作者:
    Doraiswamy PM
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Christos Davatzikos其他文献

Christos Davatzikos的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Christos Davatzikos', 18)}}的其他基金

Disentangling the anatomical, functional and clinical heterogeneity of major depression, using machine learning methods
使用机器学习方法解开重度抑郁症的解剖学、功能和临床异质性
  • 批准号:
    10714834
  • 财政年份:
    2023
  • 资助金额:
    $ 35.75万
  • 项目类别:
The Neuroimaging Brain Chart Software Suite
神经影像脑图软件套件
  • 批准号:
    10581015
  • 财政年份:
    2023
  • 资助金额:
    $ 35.75万
  • 项目类别:
Generalizable quantitative imaging and machine learning signatures in glioblastoma, for precision diagnostics and personalized treatment: the ReSPOND consortium
胶质母细胞瘤的通用定量成像和机器学习特征,用于精确诊断和个性化治疗:ReSPOND 联盟
  • 批准号:
    10625442
  • 财政年份:
    2022
  • 资助金额:
    $ 35.75万
  • 项目类别:
Generalizable quantitative imaging and machine learning signatures in glioblastoma, for precision diagnostics and personalized treatment: the ReSPOND consortium
胶质母细胞瘤的通用定量成像和机器学习特征,用于精确诊断和个性化治疗:ReSPOND 联盟
  • 批准号:
    10421222
  • 财政年份:
    2022
  • 资助金额:
    $ 35.75万
  • 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
  • 批准号:
    10696100
  • 财政年份:
    2020
  • 资助金额:
    $ 35.75万
  • 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
  • 批准号:
    10263220
  • 财政年份:
    2020
  • 资助金额:
    $ 35.75万
  • 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
  • 批准号:
    10475286
  • 财政年份:
    2020
  • 资助金额:
    $ 35.75万
  • 项目类别:
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
超大规模机器学习助力阿尔茨海默病生物库的发现
  • 批准号:
    10028746
  • 财政年份:
    2020
  • 资助金额:
    $ 35.75万
  • 项目类别:
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortium
机器学习和大规模成像分析,用于衰老和临床前阿尔茨海默氏病大脑轨迹的维度表示:大脑衰老图表和 iSTAGING 联盟
  • 批准号:
    10839623
  • 财政年份:
    2017
  • 资助金额:
    $ 35.75万
  • 项目类别:
Biomedical Image Computing and Informatics Cluster
生物医学图像计算与信息学集群
  • 批准号:
    9273767
  • 财政年份:
    2017
  • 资助金额:
    $ 35.75万
  • 项目类别:

相似海外基金

Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
  • 批准号:
    MR/S03398X/2
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    Fellowship
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
  • 批准号:
    EP/Y001486/1
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    Research Grant
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
  • 批准号:
    2338423
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    Continuing Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
  • 批准号:
    MR/X03657X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
  • 批准号:
    2348066
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
  • 批准号:
    AH/Z505481/1
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10107647
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    EU-Funded
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
  • 批准号:
    2341402
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    Standard Grant
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10106221
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
  • 批准号:
    AH/Z505341/1
  • 财政年份:
    2024
  • 资助金额:
    $ 35.75万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了